A thermal power plant is a type of a power plant for generating power from fossil fuels, in which thermal energy of steam is converted into kinetic energy, usually in a multi-part steam turbine, and, furthermore, converted into electrical energy in a generator.
In such a thermal power plant, a fuel, e.g. coal, is burned in a combustion chamber, releasing heat.
The heat released thereby is taken up by a steam generator, i.e. a power plant boiler, consisting of an evaporator (part), abbreviated to evaporator, and an (optionally multi-stage) superheater (part), abbreviated to superheater.
In the steam evaporator, previously purified and prepared (feed) water, fed therein, is converted into steam/high-pressure steam.
By further heating of the steam/high-pressure steam in the superheater, the steam is brought to the temperature necessary for the “consumer”, wherein temperature and specific volume of the steam increase. The steam is superheated by guiding the steam in a number of stages through heated tube bundles—the so-called superheater stages.
The high-pressure (fresh) steam generated thus then enters a—usually multi-part—steam turbine in the thermal power plant and there it performs mechanical work while expanding and cooling.
For the purposes of closed-loop control of thermal power plants, i.e. for closed-loop control there of (physical) state variables, such as temperature or pressure, of the feedwater or the (fresh) steam, it is known to provide for each control task, as a matter of principle, a single and uniquely assigned controller (single-variable state controller/closed-loop control; single input single output controller/control loop (SISO)).
By way of example, such a (single variable state) control of the steam temperature (controlled variable) in a thermal power plant is brought about by injecting water (manipulated variable) into the steam line upstream of the steam generator or upstream of the evaporator and the superheater stages by means of corresponding injection valves of an injection cooler. A (further) (single variable state) control of the steam pressure (controlled variable) in the thermal power plant is brought about, for example, by feeding fuel/a fuel mass flow rate (manipulated variable) into the combustion chamber of the steam generator.
EP 2 244 011 A1 has disclosed such a (single variable) state control of the steam temperature (with the injection mass flow rate as manipulated variable) in a thermal power plant.
This (single variable) state control in EP 2 244 011 A1 provides a linear quadratic regulator (LQR).
The LQR is a state controller, the parameters of which are determined in such a way that a quality criterion for the control quality is optimized.
Here, the quality criterion for linear quadratic closed-loop control also considers the relationship of the variables: the manipulated variable u and the controlled variable y. Here, the priorities can be determined by the Qy and R matrices. The quality value J is determined according to:J(x0,u(t))=∫0∞(y′(t)Qyy(t)+u′(t)Ru(t))dt. 
The static optimization problem in this respect, which is solved by the linear quadratic closed-loop control, is as follows (with K as controller matrix and x0 as initial state):
            min              u        ⁡                  (          t          )                      ⁢          J      ⁡              (                              x            0                    ,                      u            ⁡                          (              t              )                                      )              =                    min                              u            ⁡                          (              t              )                                =                      -                          Kx              ⁡                              (                t                )                                                        ⁢              J        ⁡                  (                                    x              0                        ,                          u              ⁡                              (                t                )                                              )                      =                  min        K            ⁢                        J          ⁡                      (                                          x                0                            ,                              -                                  Kx                  ⁡                                      (                    t                    )                                                                        )                          .            
Furthermore, the practice of estimating state variables, such as steam states/temperatures in the superheater, which are used in a (single variable) state control but are not measurable, using an observer circuit or using an observer (state observer) is known.
In EP 2 244 011 A1, a Kalman filter, which is likewise designed according to the LQR principle, is used as an observer for such non-measurable steam states/temperatures in the superheater of the thermal power plant. The interaction between the LQR and the Kalman filter is referred to as an LQG (linear quadratic Gaussian) algorithm.
However, the LQG method employed—according to EP 2 244 011 A1—relates to a linear control problem, whereas the injection rate of mass flow as a manipulated variable of the (single variable) state control acts on the controlled variable temperature in a nonlinear manner.
As a result of a systematic conversion of all temperature measured values and temperature reference values to enthalpies—which is furthermore also provided in EP 2 244 011 A1—a linearization of the control problem is achieved since there is a linear relationship between the injection rate of mass flow and the steam enthalpy.
Here, the conversion—from temperature to enthalpy—is brought about with the aid of corresponding water/steam table relationships using a measured steam pressure.
The calculation of a feedback matrix in the state controller (controller matrix) is brought about in a continuously online manner in EP 2 244 011 A1, using the respectively current measured values, as is also the case for the corresponding feedback matrix in the observer (observer matrix), which is set up accordingly according to the LQR principle of the state controller, by means of which observer the controller is ultimately represented.
As a result, the controller in EP 2 244 011 A1 continuously adapts to the actual operating conditions of the thermal power plant. By way of example, a load-dependent change in the dynamic superheater behavior is automatically accounted for thereby.
The robustness of the closed-loop control algorithm is thus increased in EP 2 244 011 A1 by this online calculation of the feedback matrix.
Disturbances that have a direct effect on the superheater are expressed by the fact that a heat-up range, i.e. a ratio of the enthalpies between superheater output and superheater input, is modified.
Therefore, EP 2 244 011 A1 provides not only for estimating the states or the temperatures along the superheater (state observer) but also for additionally defining the disturbance or disturbance variable as a further state and estimating the latter with the aid of the observer (disturbance variable observer).
Consequently, a very quick, accurate and simultaneously robust reaction to corresponding disturbances is possible.
Since this control algorithm according to EP 2 244 011 A1 is very robust as a result of the described measures (linearization, online calculation, disturbance variable estimation), only very few parameters need to be set when putting a thermal power plant into operation.
Startup time and complexity are therefore significantly reduced.
However, since the plurality of (but single) control loops of the individual (single variable) state controls—like, for example, in the thermal power plant—are coupled to one another by means of a common controlled system, such as the steam generator, there necessarily is mutual influencing of the individual controllers.
By way of example, the closed-loop control of the pressure in the combustion chamber of the thermal power plant by way of a suction draft is strongly influenced by the closed-loop control of a fresh-air supply via the fresh air fan of the thermal power plant. Furthermore, an increased fuel rate of mass flow in the thermal power plant results in not only an increased production of steam, but also influences the steam temperature in the thermal power plant, which steam temperature is intended to be kept constant with the aid of injections. Additionally, the closed-loop control of the feedwater rate of mass flow with the aid of the feed pump and the regulation of the feedwater pressure with the aid of the feedwater control valve are dependent on one another.
One approach for taking into account such occurring cross-influences or such couplings between the individual closed-loop controls lies in targeted taking account of the couplings and the targeted application thereof.
From a control engineering point of view, this is brought about by the use of so-called decoupling networks with decoupling branches in the closed-loop control structures or between the control loops
A design, i.e. a parameterization, of the decoupling branches is dependent on an actual dynamic process behavior of the considered systems and must be performed during the startup of the (power plant) closed-loop control.
During the parameterization, plant trials are performed and measured values are established in the plant (in the process). Evaluating the trial results or the measured values then provides information in respect of which parameters are to be modified to what extent. The parameters are then adjusted manually until the closed-loop control achieves the best-possible decoupling. A further, different approach for taking into account the occurring cross-influences between the individual controllers/closed-loop controls lies in the use of multi-variable controllers, in which a plurality of state variables are regulated simultaneously (multiple input multiple output controller/control loop (MIMO)).
Here too, a design, i.e. a parameterization, of such a multi-variable state controller is dependent on an actual dynamic process behavior of the considered system.
Thus, plant trials/tests are also carried out here and measured values of the plant are established (in the process), by means of which transfer functions between the (plurality of) input variables and the plurality of output variables and, possibly, the (plurality of) disturbance variables in the multi-variable state controller are established.
Independently of whether single closed-loop controls used for the closed-loop control problem of such a system/plant are decoupled or whether a multi-variable state controller is designed, plant trials/tests and the measured values of the plant established thereby are necessary for setting the controller or controllers.
However, if such (real) plant measured values are subject to measurement inaccuracies (or can only be measured with difficulty or not at all), a controller quality also depends on the quality of the measured values (or the availability thereof).
Therefore, a validation of measured data in this respect is helpful for designing a controller for closed-loop control of a plant/process, such as of the thermal power plant or the steam generator in the thermal power plant.